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Year : 2020  |  Volume : 24  |  Issue : 2  |  Page : 91-95

Sociodemographic profile of workers in the mining industry of Jodhpur district in Rajasthan, India

Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences (AIIMS), Jodhpur, Rajasthan, India

Correspondence Address:
Prof. Pankaja Raghav
Department of Community Medicine and Family Medicine, 2nd Floor, All India Institute of Medical Sciences, Basni Industrial Area, MIA, 2nd Phase, Basni, Jodhpur - 342 005, Rajasthan
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijoem.IJOEM_288_19

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Introduction: Mining industry has grown sharply to satisfy demands of metal and minerals for rising infrastructure. Mining is hazardous, although it is among major occupations in India involving large work force. This study was conducted to assess sociodemographic profile of sandstone mine workers in Jodhpur and to identify their major social issues. Material and Methods: A cross-sectional study was conducted among mine workers of Jodhpur, Rajasthan. Data on socioeconomic status, and occupation of each family member were collected. Data were analyzed using Statistical Package for the Social Sciences (SPSS) software program, version 23.0. A value of P < 0.05 was considered as statistically significant. Results: A total of 1604 workers were enrolled in study. More than three fourth (78.7%) of them were male. Their age ranged between 14 and 82 years with a mean age of 37.62 ± 12.30 years. Mean age of female workers was significantly higher than of male workers. Average years of working for males were significantly higher than females. Majority were illiterate, proportion being higher in females. Approximately 13% mine workers married before legal age. Approximately 2.68% children under 14 years of age were involved in income generation activity. Conclusion: More than three fourth of workers were male. Mean age of female workers was significantly higher than males. Illiteracy, child marriage, and child labor were present among mine workers. Strengths and Limitations: Robust dataset, complete enumeration, large sample size.


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